Contributions à l’agrégation séquentielle robuste d’experts : Travaux sur l’erreur d’approximation et la prévision en loi. Applications à la prévision pour les marchés de l’énergie.

We are interested in online forecasting of an arbitrary sequence of observations. At each time step, some experts provide predictions of the next observation. Then, we form our prediction by combining the expert forecasts. This is the setting of online robust aggregation of experts. The goal is to ensure a small cumulative regret. In other words, we want that our cumulative loss does not exceed too much the one of the best expert. We are looking for worst-case guarantees: no stochastic assumption on the data to be predicted is made. The sequence of observations is arbitrary. A first objective of this work is to improve the prediction accuracy. We investigate several possibilities. An example is to design fully automatic procedures that can exploit simplicity of the data whenever it is present. Another example relies on working on the expert set so as to improve its diversity. A second objective of this work is to produce probabilistic predictions. We are interested in coupling the point prediction with a measure of uncertainty (i.e., interval forecasts,…). The real world applications of the above setting are multiple. Indeed, very few assumptions are made on the data. Besides, online learning that deals with data sequentially is crucial to process big data sets in real time. In this thesis, we carry out for EDF several empirical studies of energy data sets and we achieve good forecasting performance.Nous nous intéressons à prévoir séquentiellement une suite arbitraire d'observations. À chaque instant, des experts nous proposent des prévisions de la prochaine observation. Nous formons alors notre prévision en mélangeant celles des experts. C'est le cadre de l'agrégation séquentielle d'experts. L'objectif est d'assurer un faible regret cumulé. En d'autres mots, nous souhaitons que notre perte cumulée ne dépasse pas trop celle du meilleur expert sur le long terme. Nous cherchons des garanties très robustes~: aucune hypothèse stochastique sur la suite d'observations à prévoir n'est faite. Celle-ci est supposée arbitraire et nous souhaitons des garanties qui soient vérifiées quoi qu'il arrive. Un premier objectif de ce travail est l'amélioration de la performance des prévisions. Plusieurs possibilités sont proposées. Un exemple est la création d'algorithmes adaptatifs qui cherchent à s'adapter automatiquement à la difficulté de la suite à prévoir. Un autre repose sur la création de nouveaux experts à inclure au mélange pour apporter de la diversité dans l'ensemble d'experts. Un deuxième objectif de la thèse est d'assortir les prévisions d'une mesure d'incertitude, voire de prévoir des lois. Les applications pratiques sont nombreuses. En effet, très peu d'hypothèses sont faites sur les données. Le côté séquentiel permet entre autres de traiter de grands ensembles de données. Nous considérons dans cette thèse divers jeux de données du monde de l'énergie (consommation électrique, prix de l'électricité,...) pour montrer l'universalité de l'approche

Transparent government, not transparent citizens: a report on privacy and transparency for the Cabinet Office

1. Privacy is extremely important to transparency. The political legitimacy of a transparency programme will depend crucially on its ability to retain public confidence. Privacy protection should therefore be embedded in any transparency programme, rather than bolted on as an afterthought. 2. Privacy and transparency are compatible, as long as the former is carefully protected and considered at every stage. 3. Under the current transparency regime, in which public data is specifically understood not to include personal data, most data releases will not raise privacy concerns. However, some will, especially as we move toward a more demand-driven scheme. 4. Discussion about deanonymisation has been driven largely by legal considerations, with a consequent neglect of the input of the technical community. 5. There are no complete legal or technical fixes to the deanonymisation problem. We should continue to anonymise sensitive data, being initially cautious about releasing such data under the Open Government Licence while we continue to take steps to manage and research the risks of deanonymisation. Further investigation to determine the level of risk would be very welcome. 6. There should be a focus on procedures to output an auditable debate trail. Transparency about transparency – metatransparency – is essential for preserving trust and confidence. Fourteen recommendations are made to address these conclusions

Coase's Penguin, or Linux and the Nature of the Firm

The paper explains why open source software is an instance of a potentially broader phenomenon. Specifically, I suggest that nonproprietary peer-production of information and cultural materials will likely be a ubiquitous phenomenon in a pervasively networked society. I describe a number of such enterprises, at various stages of the information production value chain. These enterprises suggest that incentives to engage in nonproprietary peer production are trivial as long as enough contributors can be organized to contribute. This implies that the limit on the reach of peer production efforts is the modularity, granularity, and cost of integration of a good produced, not its total cost. I also suggest reasons to think that peer-production can have systematic advantages over both property-based markets and corporate managerial hierarchies as a method of organizing information and cultural production in a networked environment, because it is a better mechanism for clearing information about human capital available to work on existing information inputs to produce new outputs, and because it permits largers sets of agents to use larger sets of resources where there are increasing returns to the scale of both the set of agents and the set of resources available for work on projects. As capital costs and communications costs decrease in importance as factors of information production, the relative advantage of peer production in clearing human capital becomes more salient.Comment: 29th TPRC Conference, 200

Understanding, Assessing, and Mitigating Safety Risks in Artificial Intelligence Systems

Prepared for: Naval Air Warfare Development Center (NAVAIR)Traditional software safety techniques rely on validating software against a deductively defined specification of how the software should behave in particular situations. In the case of AI systems, specifications are often implicit or inductively defined. Data-driven methods are subject to sampling error since practical datasets cannot provide exhaustive coverage of all possible events in a real physical environment. Traditional software verification and validation approaches may not apply directly to these novel systems, complicating the operation of systems safety analysis (such as implemented in MIL-STD 882). However, AI offers advanced capabilities, and it is desirable to ensure the safety of systems that rely on these capabilities. When AI tech is deployed in a weapon system, robot, or planning system, unwanted events are possible. Several techniques can support the evaluation process for understanding the nature and likelihood of unwanted events in AI systems and making risk decisions on naval employment. This research considers the state of the art, evaluating which ones are most likely to be employable, usable, and correct. Techniques include software analysis, simulation environments, and mathematical determinations.Naval Air Warfare Development CenterNaval Postgraduate School, Naval Research Program (PE 0605853N/2098)Approved for public release. Distribution is unlimite

Temporal aspects of adaptive online learning: continuity and representation

Adaptive online learning, in a very broad sense, is the study of sequential decision making beyond the worst case. Compared to their classical minimax counterparts, adaptive algorithms typically require less manual tuning, while provably performing better in benign environments, or with prior knowledge. This dissertation presents new techniques for designing these algorithms. The central theme is the emphasis on the temporal nature of the problem, which has not received enough attention in the literature. The first part of the dissertation focuses on temporal continuity. While modern online learning almost exclusively studies a discrete time repeated game, it is shown that designing algorithms can be simplified, and in certain cases optimized, by scaling the game towards a continuous time limit and solving the obtained differential equation. Concretely, we develop comparator adaptive algorithms for Online Convex Optimization, achieving optimal static regret bounds in the vanilla setting and its variant with switching costs. The benefits are extended to another classical online learning problem called Learning with Expert Advice. The second part of the dissertation focuses on temporal representation. Different from the first part, here we consider the general objective of dynamic regret minimization, which forms the foundation of time series forecasting. It is shown that by introducing temporal features, the task can be transformed to static regret minimization on a user-specified representation space with growing dimension. Drawing novel connections to wavelet features, we develop a simple algorithm improving the state-of-the-art dynamic regret bound achieved by more sophisticated approaches. An application is the online fine-tuning of a black-box time series forecaster

Developments in Demographic Forecasting

This open access book presents new developments in the field of demographic forecasting, covering both mortality, fertility and migration. For each component emerging methods to forecast them are presented. Moreover, instruments for forecasting evaluation are provided. Bayesian models, nonparametric models, cohort approaches, elicitation of expert opinion, evaluation of probabilistic forecasts are some of the topics covered in the book. In addition, the book is accompanied by complementary material on the web allowing readers to practice with some of the ideas exposed in the book. Readers are encouraged to use this material to apply the new methods to their own data. The book is an important read for demographers, applied statisticians, as well as other social scientists interested or active in the field of population forecasting. Professional population forecasters in statistical agencies will find useful new ideas in various chapters

Using Information Markets to Improve Public Decision Making

Information markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. The prices in these markets provide useful information about a particular issue, such as a president's reelection probability. The purpose of this paper is to suggest how the use of information markets can improve the quality of public policy. Our central contribution is to propose an efficient way to implement well-informed policy decisions. We do this by linking and building upon the literatures on information markets and mechanism design. Our claim is that the prices in information markets can inform the mechanism design process, thereby making previously infeasible mechanisms feasible for the policy maker. Specifically, information markets make pay-for-performance contracts viable in the policy domain. Although we focus on public sector decision making, the analysis is sufficiently general to apply to a wide range of problems in private sector and not-for-profit decision making. The framework can be applied to any situation in which a decision maker has the resources, but not the necessary information and ability, to achieve his specified objective. First, we show how it is generally possible to design contracts based on different contingencies whose prices will convey useful information on the costs and benefits of a number of policy choices, ranging from regulation to public works projects. Second, we describe one way of providing incentives for self-interested agents to implement policies that maximize net social benefits. Third, we show how information markets can be used to provide a stronger foundation for implementing a variety of government oversight mechanisms, such as a regulatory budget. We also show how legislators can use traditional budgetary controls in conjunction with information markets to exercise more effective oversight. Finally, we identify and analyze the strengths and limitations of using information markets to help improve policy. To make the analysis concrete, we examine how the "Copenhagen Consensus" which makes recommendations on spending $50 billion wisely, could have benefited from applying information markets. We argue that there is a large scope for expanding the use of information markets. These markets could promote greater transparency in governmental decision making, provide more accurate estimates of the efficiency and distributional impacts of different policies, provide a better understanding of uncertainties, help with sensitivity analysis, offer a low-cost way of assessing new policy proposals, finance government projects and regulations with positive net benefits, allow those affected by specific policies the opportunity to hedge risk, and aid in the design of policies. Furthermore, information markets can help assess the value of additional research on the decision to undertake a project. At the same time, we suggest that there are important limits to the application of information markets. We also suggest how government could play an important role in the expansion of information markets and researchers could help in the development and assessment of these markets.

The GARCH-EVT-Copula model and simulation in scenario-based asset allocation

Financial market integration, in particular, portfolio allocations from advanced economies to South African markets, continues to strengthen volatility linkages and quicken volatility transmissions between participating markets. Largely as a result, South African portfolios are net recipients of returns and volatility shocks emanating from major world markets. In light of these, and other, sources of risk, this dissertation proposes a methodology to improve risk management systems in funds by building a contemporary asset allocation framework that offers practitioners an opportunity to explicitly model combinations of hypothesised global risks and the effects on their investments. The framework models portfolio return variables and their key risk driver variables separately and then joins them to model their combined dependence structure. The separate modelling of univariate and multivariate (MV) components admits the benefit of capturing the data generating processes with improved accuracy. Univariate variables were modelled using ARMA-GARCH-family structures paired with a variety of skewed and leptokurtic conditional distributions. Model residuals were fit using the Peaks-over-Threshold method from Extreme Value Theory for the tails and a non-parametric, kernel density for the interior, forming a completed semi-parametric distribution (SPD) for each variable. Asset and risk factor returns were then combined and their dependence structure jointly modelled with a MV Student t copula. Finally, the SPD margins and Student t copula were used to construct a MV meta t distribution. Monte Carlo simulations were generated from the fitted MV meta t distribution on which an out-of-sample test was conducted. The 2014-to-2015 horizon served to proxy as an out-of-sample, forward-looking scenario for a set of key risk factors against which a hypothetical, diversified portfolio was optimised. Traditional mean-variance and contemporary mean-CVaR optimisation techniques were used and their results compared. As an addendum, performance over the in-sample 2008 financial crisis was reported. The final Objective (7) addressed management and conservation strategies for the NMBM. The NMBM wetland database that was produced during this research is currently being used by the Municipality and will be added to the latest National Wetland Map. From the database, and tools developed in this research, approximately 90 wetlands have been identified as being highly vulnerable due to anthropogenic and environmental factors (Chapter 6) and should be earmarked as key conservation priority areas. Based on field experience and data collected, this study has also made conservation and rehabilitation recommendations for eight locations. Recommendations are also provided for six more wetland systems (or regions) that should be prioritised for further research, as these systems lack fundamental information on where the threat of anthropogenic activities affecting them is greatest. This study has made a significant contribution to understanding the underlying geomorphological processes in depressions, seeps and wetland flats. The desktop mapping component of this study illustrated the dominance of wetlands in the wetter parts of the Municipality. Perched wetland systems were identified in the field, on shallow bedrock, calcrete or clay. The prevalence of these perches in depressions, seeps and wetland flats also highlighted the importance of rainfall in driving wetland formation, by allowing water to pool on these perches, in the NMBM. These perches are likely to be a key factor in the high number of small, ephemeral wetlands that were observed in the study area, compared to other semi-arid regions. Therefore, this research highlights the value of multi-faceted and multi-scalar wetland research and how similar approaches should be used in future research methods has been highlighted. The approach used, along with the tools/methods developed in this study have facilitated the establishment of priority areas for conservation and management within the NMBM. Furthermore, the research approach has revealed emergent wetland properties that are only apparent when looking at different spatial scales. This research has highlighted the complex biological and geomorphological interactions between wetlands that operate over various spatial and temporal scales. As such, wetland management should occur across a wetland complex, rather than individual sites, to account for these multi-scalar influences